- From: Angelo Salatino <aas88ie@gmail.com>
- Date: Mon, 3 Jun 2024 09:10:12 +0100
- To: undisclosed-recipients:;
- Message-ID: <CAOmG_3Unuh59joNtF8Rzd2p24F9EMR4AoZ4kJ03Ktj=T4QMS8A@mail.gmail.com>
Reminder:
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CALL FOR PAPERS
Sci-K – 4th International Workshop on Scientific Knowledge Representation,
Discovery, and Assessment in conjunction with the International Semantic
Web Conference (ISWC) 2024
November 11/12 2024, Baltimore, MD, USA
Web: https://sci-k.github.io, X: @scik_workshop
<https://twitter.com/scik_workshop>
Submission deadline: July 11th, 2024
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Aim and Scope:
In the last decades, we have experienced a substantial increase in the
volume of published scientific articles and research artefacts (e.g., data
sets, software packages); this trend is expected to continue and opens up
challenges including the development of large-scale machine-readable
representations of scientific knowledge, making scholarly data discoverable
and accessible, and designing reliable and comprehensive metrics to assess
scientific impact. The main objective of Sci-K is to provide a forum for
researchers and practitioners from different disciplines to present,
educate, and guide research related to scientific knowledge. We foresee
three themes that cover the most important challenges in this field:
representation, discoverability, and assessment.
Representation. There is a need for flexible, context-sensitive,
fine-grained, and machine-actionable representations of scholarly knowledge
that are, at the same time, structured, interlinked, and semantically rich:
Scientific Knowledge Graphs (SKGs). SKGs can power data-driven services for
navigating, analysing, and making sense of research dynamics. Current
challenges are related to the design of ontologies able to conceptualise
scholarly knowledge, model its representation, and enable its exchange
across different SKGs.
Discoverability. Scholarly information should be easily findable,
discoverable, and visible so that it can be mined and organised within
SKGs. Discovery tools should be able to crawl the Web and identify
scholarly data, whether on a publisher’s website or elsewhere –
institutional repositories, preprint servers, open-access repositories, and
others. This is a particularly challenging endeavour as it requires deep
understanding of both the scholarly communication landscape and the needs
of a variety of stakeholders: researchers (of different fields and
sub-fields), publishers, funders, and the general public. Other challenges
are related to the discovery and extraction of entities and concepts,
integration of information from heterogeneous sources, identification of
duplicates, finding connections between entities, and identifying
conceptual inconsistencies.
Assessment. Due to the continuous growth in volume of research output and
limited amounts of funding, rigorous approaches for the evaluation and
assessment of research impact are now more relevant than ever. There is a
need for reliable, comprehensive, and equitable metrics and indicators of
the scientific impact and merit of publications, datasets, research
institutions, individual researchers, and other relevant entities.
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Topics of Interest:
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Representation
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Data models for the description of scholarly data and their
relationships.
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Description and use of provenance information of scientific data.
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Integration and interoperability models of different data sources.
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NLP and AI approaches that demonstrate related methods and
technologies.
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Discoverability
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Methods for extracting metadata, entities and relationships from
scientific data.
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Methods for the (semi-)automatic annotation and enhancement of
scientific data.
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Methods and interfaces for the exploration, retrieval, and
visualisation of scholarly data.
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NLP and AI approaches that demonstrate related methods and
technologies.
-
Assessment
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Novel methods, indicators, and metrics for quality and impact
assessment of scientific publications, datasets, software, and other
relevant entities based on scholarly data.
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Uses of scientific knowledge graphs and citation networks for the
facilitation of research assessment.
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Studies regarding the characteristics or the evolution of scientific
impact or merit.
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NLP and AI approaches that demonstrate related methods and
technologies.
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Submission Guidelines:
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Full research papers (up to 8 pages for main content)
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Short research papers (up to 4 pages for main content)
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Vision/Position papers (up to 4 pages for main content)
The workshop calls for full research papers (up to 8 pages + 2 pages of
appendices + 2 pages of references), describing original work on the listed
topics, and short papers (up to 4 pages + 2 pages of appendices + 2 pages
of references), on early research results, new results on previously
published works, demos, and projects. In accordance with Open Science
principles, research papers may also be in the form of data or software
papers (short or long papers). Data papers present the motivation and
methodology behind the creation of data sets that are of value to the
community, e.g., annotated corpora, benchmark collections, and training
sets. Software papers present software functionality, its value for the
community, and its application. To enable reproducibility and peer-review,
authors are requested to share the DOIs of datasets and software products
described in the articles.
The workshop also calls for vision/position papers (up to 4 pages + 2 pages
of appendices + 2 pages of references) providing insights towards new or
emerging areas, innovative or risky approaches, or emerging applications
that will require extensions to the state of the art. Vision papers do not
necessarily have to present results but should carefully elaborate on the
motivation and ongoing challenges of the described area.
Sci-K will adopt a single-blind review process, and each paper will be
reviewed by at least three Program Committee members.
Submissions must be in PDF format and must adhere to the CEURART
single-column template. Submissions that do not follow these guidelines, or
do not view or print properly, may be rejected without review.
The proceedings of the workshops will be published on CEUR (indexed in
Scopus, DBLP and so on.)
Submit your contributions following the link:
https://sci-k.github.io/2024/#submission
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Important Dates:
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Paper submission: July 11th, 2024 (23:59, AoE timezone)
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Notification of acceptance: August 8th, 2024
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Camera-ready due: August 25th, 2024 (23:59, AoE timezone)
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Workshop day: November 11/12, 2024 (TBA)
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Organizing Committee (alphabetical order):
Andrea Mannocci, CNR-ISTI, Italy
Francesco Osborne, The Open University, UK
Georg Rehm, DFKI, Germany
Angelo Salatino, The Open University, UK
Sonja Schimmler, TU Berlin, Fraunhofer FOKUS, Germany
Received on Monday, 3 June 2024 08:10:30 UTC